|SAHA, UTTAM - University Of Georgia
|Tillman, Patricia - Glynn
|Johnson, Wiley - Carroll
|GASKIN, JULIA - University Of Georgia
|SONON, LETICIA - University Of Georgia
|YANG, YUANGEN - University Of Georgia
Submitted to: American Journal of Analytical Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/4/2017
Publication Date: 7/7/2017
Citation: Saha, U., Endale, D.M., Tillman, P.G., Johnson, W.C., Gaskin, J., Sonon, L., Schomberg, H.H., Yang, Y. 2017. Analysis of various quality attributes of sunflower and soybean plants by near infra-red reflectance spectroscopy: Development and validation of calibration models. American Journal of Analytical Chemistry. 8:462-492. Https://doi.org/10.4236/ajac.2017.87035.
Interpretive Summary: Soybean and sunflower are summer annuals that can be grown as an alternative to corn and may be particularly useful as forage crops in addition to their traditional use as protein and/or oil yielding crops. In recent years, global demand for livestock products has increased. There has also been an even greater increase in demand for organic meat. Meeting this increasing consumer demand has placed a premium on quality sources of organic grain and forage for livestock feed. Rapid and low cost methods of accurately determining forage quality would be advantageous for producers in determining the value of the forage (nutritional and commercial). In integrated forage based livestock production industries, high throughput in analysis is important for diagnosing and correcting plant nutrient deficiency and evaluating forage quality. A non-destructive spectroscopic sensing technique such as near infra-red spectroscopy (NIRS) has been shown to be a suitable analytical technique for this purpose as compared to traditional laboratory analysis using wet chemistry which is expensive and time consuming. Limited data are available for NIRS analysis of sunflower and soybean crops grown under organic production methods. In this study, we developed and validated NIRS calibration models for 27 different constituents of the leaves and reproductive parts for organically grown soybean and sunflower. All but one model for starch were found to be good or better in their quantitative predictability. Thus these models can be reliably applied in routine analysis for those constituents.
Technical Abstract: Soybean and sunflower are summer annuals that can be grown as an alternative to corn and may be particularly useful in organic production systems for forage in addition to their traditional use as protein and/or oil yielding crops. Rapid and low cost methods of analyzing plant quality would be helpful for crop management. We developed and validated calibration models for Near-infrared Reflectance Spectroscopic (NIRS) analysis of 27 different constituents belonging to proximate and plant-nutrient composition of sunflower and soybean leaves or reproductive parts. From a population of 120 samples, calibration models were developed utilizing spectral information covering both visible and NIR region of 61-85 randomly chosen samples for various constituents using modified partial least-squares (MPLS) regression with internal cross validation. Within MPLS protocol, we compared 9 different math treatments on the quality of the calibration models developed. The math treatment “2,4,4,1” yielded the best quality models for the most parameters. These models had low standard error of both calibration (SEC) and cross-validation (SECV) with high coefficient of determination in both calibration (R2 = 0.6993-0.9986) and cross validation (1-VR = 0.6181-0.9966) for all parameters except starch and simple sugars. Prediction of an independent validation set of 28-35 samples with these models yielded excellent agreement between the NIRS predicted values and the reference values based on the low standard error of prediction (SEP), low bias, high coefficient of determination (r2 = 0.8260-0.9990), and high ratios of both performance to deviation (RPD = SD/SEP; 2.34-28.78) and performance to inter-quartile distance (RPIQ = IQ/SEP; 2.18-11.94) for all 27 parameters except starch, indicating the 26 calibration models had acceptable quantitative information.